@annabavaresco.bsky.social and @tlmnhut.bsky.social show: supervised pruning of a DNN’s feature space better aligns with human category representations, selects distinct subspaces for different categories, and more accurately predicts people’s preferences for GenAI images. doi.org/10.1145/3768...

Come and check out our poster at #CCN2025, presented by @tlmnhut.bsky.social

Interested in category selectivity and topographic modelling? Come see my poster tomorrow at CCN (A57). We show that encoding models confirm dissociable selective responses to bodies, hands, and tools, and test if topographic ANNs capture that organization. See you there!

Missed #CCN2025 this year, but still excited to share two works there! 1️⃣ From my PhD with @manpiazza.bsky.social — accepted in the CCN proceedings. My young collaborator @tlmnhut.bsky.social will be presenting it. It’s about numerosity representation in CNNs. 📄 tinyurl.com/yc2dyhm3

New lab preprint, led by @tlmnhut.bsky.social. We show that certain topographic CNNs offer computational advantages, including greater weight matrix robustness, better handling of OOD noisy data, and higher entropy of unit activation. arxiv.org/abs/2508.00043

New preprint out! We propose that action is a key dimension shaping the topographic organization of object categories in lateral occipitotemporal cortex (LOTC)—and test whether standard and topographic neural networks capture this pattern. A thread: www.biorxiv.org/content/10.1... 🧵 1/n

DNNs can predict human similarity judgments—but why? In a new #XAI study, we introduce Alignment Importance Scores (AIS), a method that improves AI-human alignment and generates heatmaps highlighting the image features that drive this alignment. link.springer.com/article/10.1...

Excited to share our work with @tlmnhut.bsky.social at the NeurIPS Workshop on Behavioral Machine Learning! 🧠 Come visit our poster! #NeurIPS2024 #BehavioralML #Numerosity #DeepLearning

(1/2) We'll be presenting two recent projects in the ICLR 2024 Re-Align Workshop. PHD students Nhut Truong and Dario Pesenti introduce an explainability technique indicating what image information is relevant when it is compared to a target image cohort. openreview.net/forum?id=bWe... #cogsci

An updated version of our work on using feature maps of pre-trained DNNs to explain human similarity judgments; now on arXiv. arxiv.org/abs/2409.16292